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plemio

ANSES Ciqual MCP Server

by plemio
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool named 'query', there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as executing SQL queries on the ANSES Ciqual database, making it straightforward for an agent to select.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'query' follows a simple, clear verb pattern that aligns with its function, and there are no other tools to cause inconsistency.

    Tool Count2/5

    The server has only one tool, which is too few for its apparent scope of providing access to a complex food composition database with multiple tables and query types. A single SQL query tool places excessive burden on the agent to construct correct queries, lacking specialized tools for common operations like searching foods or retrieving nutrients.

    Completeness2/5

    The tool surface is severely incomplete for the domain. While the 'query' tool allows access to all data, it lacks dedicated tools for key operations such as food search, nutrient lookup, or retrieving specific food compositions, which are essential for a food database. This forces agents to handle complex SQL, increasing the risk of errors and inefficiencies.

  • Average 4.8/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the database is read-only and restricts usage to SELECT queries, which informs the agent about safety and limitations. It also provides context on database structure (tables like foods, nutrients, composition), example queries, and performance tips (e.g., avoiding multiple queries), adding significant value beyond any structured fields.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with clear sections (e.g., IMPORTANT, EXAMPLE, SCHEMA, COMMON QUERIES, KEY NUTRIENT CODES) and uses bullet points for readability. It is appropriately sized for a complex tool, but some parts (like the detailed schema listing) could be slightly condensed. Every sentence adds value, such as performance advice and database constraints, making it efficient overall.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (executing SQL queries on a specific database), no annotations, 0% schema coverage, but with an output schema present, the description is highly complete. It covers purpose, usage guidelines, behavioral traits (read-only, SELECT-only), parameter semantics with examples, database structure, and common queries. The output schema handles return values, so the description doesn't need to explain them, making it fully adequate for the agent's needs.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 0% description coverage for the single parameter 'sql', so the description must compensate. It adds substantial meaning by explaining that 'sql' should be an SQL query for the ANSES Ciqual database, providing example queries, schema details (tables and columns), and usage tips. However, it doesn't explicitly define the 'sql' parameter's syntax or constraints beyond examples, leaving some room for interpretation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description explicitly states the tool's purpose: 'Execute SQL query on ANSES Ciqual French food composition database.' It specifies the verb ('Execute SQL query'), the resource ('ANSES Ciqual French food composition database'), and distinguishes it from potential alternatives by emphasizing 'Get ALL nutrients in ONE query! Don't make multiple queries for the same food.' This is specific and clear, with no siblings to differentiate from.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit guidance on when to use this tool: for executing SQL queries on the specified database. It includes detailed examples (e.g., 'EXAMPLE - Get complete nutrition for a food'), common queries (e.g., 'Search foods', 'Get ALL nutrients'), and key constraints ('The database is read-only. Use SELECT queries only.'). This covers when to use it, how to use it effectively, and what not to do, with no alternatives mentioned as there are no sibling tools.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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